背景:对恶病质的预防和治疗具有重要意义。然而,如何识别恶习前仍然是一个挑战。
目的:本研究旨在使用一种简单的方法来检测癌症的前病质,并区分前病质和恶病质的不同特征。
方法:我们纳入了这项研究的3896名参与者。我们使用所有基线特征作为输入变量,并使用经过训练的机器学习(ML)模型来计算变量的重要性。根据变量的重要性过滤后,模型被重新训练。基于接收机工作特征值选择最佳模型。随后,我们使用相同的方法和过程,在非恶病质人群中,我们使用相同的方法和过程来鉴别出存在恶病质前的患者.
结果:这项研究的参与者包括2228名男性(57.2%)和1668名女性(42.8%),其中有471人被诊断出患有孔雀病,1178患有恶病质,其余为非恶病质。恶病质最重要的特征是饮食变化,臂围,高密度脂蛋白(HDL)水平,和C反应蛋白白蛋白比(CAR)。区分恶习的最重要特征是饮食变化,血清肌酐,HDL,握力,和汽车。用于筛查恶病质和诊断恶病质的两个逻辑回归模型的曲线下面积最大,分别为0.830和0.701。校正和判定曲线表明模型具有良好的准确性。
结论:我们开发了两个模型来识别前病质和恶病质,这将有助于临床医生检测和诊断恶习。
BACKGROUND: Detection of precachexia is important for the prevention and treatment of
cachexia. However, how to identify precachexia is still a challenge.
OBJECTIVE: This study aimed to detect cancer precachexia using a simple method and distinguish the different characteristics of precachexia and
cachexia.
METHODS: We included 3896 participants in this study. We used all baseline characteristics as input variables and trained machine learning (ML) models to calculate the importance of the variables. After filtering the variables based on their importance, the models were retrained. The best model was selected based on the receiver operating characteristic value. Subsequently, we used the same method and process to identify patients with precachexia in a noncachexia population using the same method and process.
RESULTS: Participants in this study included 2228 men (57.2%) and 1668 women (42.8%), of whom 471 were diagnosed with precachexia, 1178 with cachexia, and the remainder with noncachexia. The most important characteristics of
cachexia were eating changes, arm circumference, high-density lipoprotein (HDL) level, and C-reactive protein albumin ratio (CAR). The most important features distinguishing precachexia were eating changes, serum creatinine, HDL, handgrip strength, and CAR. The two logistic regression models for screening for
cachexia and diagnosing precachexia had the highest area under the curve values of 0.830 and 0.701, respectively. Calibration and decision curves showed that the models had good accuracy.
CONCLUSIONS: We developed two models for identifying precachexia and
cachexia, which will help clinicians detect and diagnose precachexia.